# detr-resnet50¶

## Use Case and High-Level Description¶

The detr-resnet50 model is one from DEtection TRansformer (DETR) models family, which consider object detection as a direct set prediction problem. The model has ResNet50 backbone and pretrained on Common Objects in Context (COCO) dataset for solving object detection task. DETR predicts all objects at once, and is trained end-to-end with a set loss function which performs bipartite matching between predicted and ground-truth objects. DETR simplifies the detection pipeline by dropping multiple hand-designed components that encode prior knowledge, like spatial anchors or non-maximal suppression.

More details provided in the paper and repository.

Metric

Value

Type

Object detection

GFLOPs

174.4708

MParams

41.3293

Source framework

PyTorch*

## Accuracy¶

Accuracy metrics obtained on Common Objects in Context (COCO) validation dataset for converted model. Background label + label map with 80 public available object categories are used (original indexing to 91 categories is preserved).

Metric

Value

coco_orig_precision

39.27%

coco_precision

42.36%

## Input¶

### Original model¶

Image, name - input, shape - 1, 3, 800, 1137, format B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order is RGB.

Mean values - [123.675, 116.28, 103.53]. Scale values - [58.395, 57.12, 57.375].

### Converted model¶

Image, name - input, shape - 1, 3, 800, 1137, format B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order is BGR.

## Output¶

### Original model¶

1. Bounding boxes, name: boxes, shape - 1, 100, 4. Presented in format B, A, 4, where:

• B - batch size

• A - number of detected anchors

For each detection, the description has the format: [x, y, w, h], where:

• (x, y) - coordinates of the bounding box center(coordinates are in normalized format, in range [0, 1])

• w - width of bounding box(values are in normalized format, in range [0, 1])

• h - height of bounding box(values are in normalized format, in range [0, 1])

2. Scores, name: scores, shape - 1, 100, 92. Contains scores for 91 Common Objects in Context (COCO) object classes. The last class is no-object class.

### Converted model¶

1. Bounding boxes, name: boxes, shape - 1, 100, 4. Presented in format B, A, 4, where:

• B - batch size

• A - number of detected anchors

For each detection, the description has the format: [x, y, w, h], where:

• (x, y) - coordinates of the bounding box center(coordinates are in normalized format, in range [0, 1])

• w - width of bounding box(values are in normalized format, in range [0, 1])

• h - height of bounding box(values are in normalized format, in range [0, 1])

2. Scores, name: scores, shape - 1, 100, 92. Contains scores for 91 Common Objects in Context (COCO) object classes. The last class is no-object class.

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

omz_downloader --name <model_name>
omz_converter --name <model_name>